Reproductive cycles of two island pitvipers species (Serpentes, Viperidae) determined by ultrasonography and radiography.

We used ultrasonography and radiography to assess the sexual organs and characterize the reproductive cycle of captive golden lancehead (Bothrops insularis) and Alcatrazes lancehead (B. alcatraz), two endangered island snake species in Brazil. We assessed 46- individuals of golden lancehead and 12 of Alcatrazes lancehead kept in captivity between 2014 and 2020. Follicular development was similar between species, but follicles in Alcatrazes lancehead were smaller than in the golden lanceheads. Female golden lanceheads produced 24 live young, seven stillborn and 73 undeveloped eggs. Parturition of live young occurred between midsummer (February) and early autumn and gestation averaged 8 months. Female Alcatrazes lanceheads produced four live young in midsummer, and one undeveloped egg in early autumn. Males and females of both species have seasonal and biennial reproductive cycles. Sperm storage in both sexes is essential to coordinate male and female cycles. The data obtained with golden lancehead and Alcatrazes lancehead in captivity, demonstrate a degree of conservatism, following data from other Bothrops.

Comparison of imageless robotic assisted total knee arthroplasty and conventional total knee arthroplasty: early clinical and radiological outcomes of 200 knees.

The aim of this study was to compare the short term clinical and radiological outcomes of imageless robotic and conventional total knee arthroplasty (TKA) and to estimate the accuracy of the two techniques by analysing the outliers after TKA. We have evaluated 200 consecutive knees (158 patients), 100 knees undergoing robotic TKA, and 100 knees treated with conventional TKA. Demographic parameters like age, gender, body mass index, diagnosis and range of motion were obtained. Knee society score (KSS) and Knee society functional score (KSS-F) were used for clinical evaluation. Mechanical alignment (Hip-knee-ankle angle), proximal tibial angle (MPTA), distal femoral angle (LDFA) and tibial slope were analysed for radiological results and outliers were compared between both groups. Outliers were defined when the measured angle exceeded ± 3° from the neutral alignment in each radiological measurement on the final follow-up radiograph.The minimum follow-up was 6 months (range, 6 to 18 months). The preoperative mean HKA angle was 169.7 ± 11.3° in robotic group and 169.3 ± 7.3° in conventional group. There was significant improvement in HKA, LDFA, MPTA and tibial slope compared to the preoperative values in both the groups (p < 0.01). The number of HKA, LDFA and tibial slope outliers were 31, 29 and 37, respectively, in the conventional group compared to 13, 23 and 17 in the robotic group (p < 0.01). There was a significant improvement in the KSS and KSS-F functional scores postoperatively in both the groups (p < 0.01). However, there was no significant difference in the functional scores between the groups postoperatively (p = 0.08). This study showed excellent improvement with both imageless robotic and conventional TKA, with similar clinical outcomes between both groups. However, radiologically robotic TKA showed better accuracy and consistency with fewer outliers compared with conventional TKA.

Non-traumatic complete cervical spine dislocation with severe fixed kyphosis: successful multidisciplinary approach to a challenging case.

BACKGROUND: To our knowledge, there is no previous report in the literature of non-traumatic neglected complete cervical spine dislocation characterized by anterior spondyloptosis of C4, extreme head drop, and irreducible cervicothoracic kyphosis. CASE PRESENTATION: We report the case of a 33-year-old Caucasian man with a 17-year history of severe immune polymyositis and regular physiotherapy who presented with severe non-reducible kyphosis of the cervicothoracic junction and progressive tetraparesia for several weeks after a physiotherapy session. Radiographs, computed tomography, and magnetic resonance imaging revealed a complete dislocation at the C4-C5 level, with C4 spondyloptosis, kyphotic angulation, spinal cord compression, and severe myelopathy. Due to recent worsening of neurological symptoms, an invasive treatment strategy was indicated. The patient's neurological status and spinal deformity greatly complicated the anesthetic and surgical management, which was planned after extensive multidisciplinary discussion and relied on close collaboration between the orthopedic surgeon and the anesthetist. Regarding anesthesia, difficult airway access was expected due to severe cervical angulation, limited mouth opening, and thyromental distance, with high risk of difficult ventilation and intubation. Patient management was further complicated by a theoretical risk of neurogenic shock, motor and sensory deterioration, instability due to position changes during surgery, and postoperative respiratory failure. Regarding surgery, a multistage approach was carefully planned. After a failed attempt at closed reduction, a three-stage surgical procedure was performed to reduce displacement and stabilize the spine, resulting in correct spinal realignment and fixation. Progressive complete neurological recovery was observed. CONCLUSION: This case illustrates the successful management of a critical situation based on a multidisciplinary collaboration involving radiologists, anesthesiologists, and spine surgeons.

Ensemble detection of hand joint ankylosis and subluxation in radiographic images using deep neural networks.

The modified total Sharp score (mTSS) is often used as an evaluation index for joint destruction caused by rheumatoid arthritis. In this study, special findings (ankylosis, subluxation, and dislocation) are detected to estimate the efficacy of mTSS by using deep neural networks (DNNs). The proposed method detects and classifies finger joint regions using an ensemble mechanism. This integrates multiple DNN detection models, specifically single shot multibox detectors, using different training data for each special finding. For the learning phase, we prepared a total of 260 hand X-ray images, in which proximal interphalangeal (PIP) and metacarpophalangeal (MP) joints were annotated with mTSS by skilled rheumatologists and radiologists. We evaluated our model using five-fold cross-validation. The proposed model produced a higher detection accuracy, recall, precision, specificity, F-value, and intersection over union than individual detection models for both ankylosis and subluxation detection, with a detection rate above 99.8% for the MP and PIP joint regions. Our future research will aim at the development of an automatic diagnosis system that uses the proposed mTSS model to estimate the erosion and joint space narrowing score.

Enhancing fracture diagnosis in pelvic X-rays by deep convolutional neural network with synthesized images from 3D-CT.

Pelvic fractures pose significant challenges in medical diagnosis due to the complex structure of the pelvic bones. Timely diagnosis of pelvic fractures is critical to reduce complications and mortality rates. While computed tomography (CT) is highly accurate in detecting pelvic fractures, the initial diagnostic procedure usually involves pelvic X-rays (PXR). In recent years, many deep learning-based methods have been developed utilizing ImageNet-based transfer learning for diagnosing hip and pelvic fractures. However, the ImageNet dataset contains natural RGB images which are different than PXR. In this study, we proposed a two-step transfer learning approach that improved the diagnosis of pelvic fractures in PXR images. The first step involved training a deep convolutional neural network (DCNN) using synthesized PXR images derived from 3D-CT by digitally reconstructed radiographs (DRR). In the second step, the classification layers of the DCNN were fine-tuned using acquired PXR images. The performance of the proposed method was compared with the conventional ImageNet-based transfer learning method. Experimental results demonstrated that the proposed DRR-based method, using 20 synthesized PXR images for each CT, achieved superior performance with the area under the receiver operating characteristic curves (AUROCs) of 0.9327 and 0.8014 for visible and invisible fractures, respectively. The ImageNet-based method yields AUROCs of 0.8908 and 0.7308 for visible and invisible fractures, respectively.

High prevalence of radiographic erosions in early, untreated PsA: results from the SpARRO cohort.

AIMS: To investigate the prevalence and distribution of bone erosions in an early psoriatic arthritis (PsA) population using conventional radiography (CR) and to explore the agreement between CR and ultrasound (US) detected bone erosions. METHODS: Newly diagnosed, treatment naïve PsA patients fulfilling the ClASsification for Psoriatic Arthritis (CASPAR) classification criteria of ≤5 years symptom duration were recruited as part of the Leeds Spondyloarthropathy Register for Research and Observation and underwent CR and US examination of hands and feet. RESULTS: Overall, 4655 hand and feet joints were assessed in 122 patients. CR erosions were detected in 24.6% (n=30) with lowest prevalence seen below 8 months of symptoms (17.5% vs 24.3%>24 months). The number of erosions was higher on CR (1.55% (63/4,655); US 1.04% (34/3,270)), with 5th metatarsophalangeal (MTP) joint being the most affected site in both CR (5.21% (11/211)) and US (7.14% (15/210)). Erosions in CR were more evenly distributed compared with US where three-quarters of the total number of bone erosions were detected in wrists, second metacarpophalangeal (MCP) and fifth MTP joints. Most joints had almost perfect prevalence-adjusted bias-adjusted kappa values ranging from 0.91 to 1. CONCLUSIONS: Erosions were seen in a quarter of patients with newly diagnosed, untreated PsA with a declining trend around the 8-month symptom duration cut-off. High levels of agreement between CR and US were seen with CR detecting more erosions. A focused US assessment of the wrist, second MCP and fifth MTP joints may be useful to detect bone erosions in early PsA.

Diagnostic accuracy in axial spondyloarthritis: a systematic evaluation of the role of clinical information in the interpretation of sacroiliac joint imaging.

OBJECTIVES: Radiography and MRI of the sacroiliac joints (SIJ) are relevant for the diagnosis and classification of patients with axial spondyloarthritis (axSpA). This study aimed to evaluate the impact of clinical information (CI) on the accuracy of imaging interpretation. METHODS: Out of 109 patients referred because of suspicion of axSpA with complete imaging sets (radiographs and MRI of SIJ), 61 were diagnosed with axSpA (56%). Images were independently evaluated by three radiologists in four consecutive reading campaigns: radiographs and radiographs+MRI without and with CI including demographic data, SpA features, physical activity and pregnancy. Radiographs were scored according to the modified New York criteria, and MRIs for inflammatory and structural changes compatible with axSpA (yes/no). The clinical diagnosis was taken as reference standard. The compatibility of imaging findings with a diagnosis of axSpA (precision) before and after the provision of CI and radiologists' confidence with their findings (0-10) were evaluated. RESULTS: The precision of radiographs evaluation without versus with CI increased from 70% to 78% (p=0.008), and for radiographs+MRI from 81% to 82% (p=1.0), respectively. For CR alone, the sensitivity and specificity of radiologic findings were 51% and 94% without and 60% and 100% with CI, while, for radiographs+MRI, they were 74% and 90% vs 71% and 98%, respectively. The diagnostic confidence of radiologists increased from 5.2±1.9 to 6.0±1.7 with CI for radiographs, and from 6.7±1.6 to 7.2±1.6 for radiographs+MRI, respectively. CONCLUSION: The precision, specificity and diagnostic confidence of radiologic evaluation increased when CI was provided.

From explanation to intervention: Interactive knowledge extraction from Convolutional Neural Networks used in radiology.

Deep Learning models such as Convolutional Neural Networks (CNNs) are very effective at extracting complex image features from medical X-rays. However, the limited interpretability of CNNs has hampered their deployment in medical settings as they failed to gain trust among clinicians. In this work, we propose an interactive framework to allow clinicians to ask what-if questions and intervene in the decisions of a CNN, with the aim of increasing trust in the system. The framework translates a layer of a trained CNN into a measurable and compact set of symbolic rules. Expert interactions with visualizations of the rules promote the use of clinically-relevant CNN kernels and attach meaning to the rules. The definition and relevance of the kernels are supported by radiomics analyses and permutation evaluations, respectively. CNN kernels that do not have a clinically-meaningful interpretation are removed without affecting model performance. By allowing clinicians to evaluate the impact of adding or removing kernels from the rule set, our approach produces an interpretable refinement of the data-driven CNN in alignment with medical best practice.

Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study.

BACKGROUND: The study aimed to develop and validate a deep learning-based Computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States. METHODS: In this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists. RESULTS: The CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong's CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings. CONCLUSION: This study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings.

Optimization of the clinical diagnostic examination algorithm of patients with a cross bite complicated by cranio-mandibular dysfunction and postural disorder.

OBJECTIVE: Aim: Optimization of the clinical and diagnostic examination algorithm of patients with cross bite, aggravated by cranio-mandibular dysfunction and postural disorders. PATIENTS AND METHODS: Materials and Methods: 22 patients aged 13-16 years with cross bite with displacement of the lower jaw were examined. The first group consisted of 15 people with a right-sided displacement of the lower jaw, the second - 7 patients with a left-sided one. The condition of the musculoskeletal system was assessed by the position of the head, shoulders, shoulder blades, back (curvature of the spine), legs, chest shape, and abdomen. To determine the state of stability of the body in space, posturological and kinesiological tests were performed. The location of TMJ elements was evaluated on orthopantomograms. Statistical processing of the material was carried out with the help of the "Excel" license package. RESULTS: Results: 63.64% of patients with a cross bite have disorders of the musculoskeletal system: scoliotic posture - 40.91% and scoliosis - 22.73%. TMJ dysfunction was detected in all examined patients. It was established that the anatomical and topographic features of the joint elements depend on the side of the lower jaw displacement. CONCLUSION: Conclusions: The functional imbalance of all structural elements of the musculoskeletal system and the cranio-mandibular complex determined during the research proved the need to optimize the clinical-diagnostic algorithm: consultation of a traumatologist-orthopedic doctor, X-ray examination of TMJ, conducting posturological tests.

Target sign in COVID-19, radiological interpretation and diagnostic contribution of digital thoracic tomosynthesis.

INTRODUCTION: Our objectives are: To describe the radiological semiology, clinical-analytical features and prognosis related to the target sign (TS) in COVID-19. To determine whether digital thoracic tomosynthesis (DTT) improves the diagnostic ability of radiography. MATERIAL AND METHODS: Retrospective, descriptive, single-centre, case series study, accepted by our ethical committee. Radiological, clinical, analytical and follow-up characteristics of patients with COVID-19 and TS on radiography and DTT between November 2020 and January 2021 were analysed. RESULTS: Eleven TS were collected in 7 patients, median age 35 years, 57% male. All TS presented with a central nodule and a peripheral ring, and in at least 82%, the lung in between was of normal density. All TS were located in peripheral, basal regions and 91% in posterior regions. TS were multiple in 43%. Contiguous TS shared the peripheral ring. Other findings related to pneumonia were associated in 86% of patients. DTT detected 82% more TS than radiography. Only one patient underwent a CT angiography of the pulmonary arteries, positive for acute pulmonary thromboembolism. Seventy-one per cent presented with pleuritic pain. No distinctive laboratory findings or prognostic worsening were detected. CONCLUSIONS: TS in COVID-19 predominates in peripheral and declining regions and can be multiple. Pulmonary thromboembolism was detected in one case. It occurs in young people, frequently with pleuritic pain and does not worsen the prognosis. DTT detects more than 80 % of TS than radiography.

Bilateral juvenile osteochondrosis dissecans in monozygotic twins: a case report.

INTRODUCTION: The etiology of osteochondrosis dissecans (OCD), a chondropathy associated with detachment of the subchondral bone and the overlaying cartilage, is not yet fully understood. While repetitive physical exercise-related stress is usually assumed to be the main risk factor for the occurrence of OCD, genetic predisposition could have an underestimated influence on the development of the disease. CASE REPORT: We report a case of monozygotic twins with almost identical stages of bilateral osteochondrosis dissecans of the knee joint. In both patients, initially, a unilateral lesion occurred; despite restricted physical exercise, in the further course of the disease a lesion also developed on the contralateral side. While the lesion found most recently demonstrated an ongoing healing process at a 6-month follow-up, the other three lesions showed a natural course of healing under conservative treatment with significant clinical as well as radiological improvements after one year and complete consolidation in magnetic resonance imaging (MRI) after 2 years. CONCLUSION: There could be a genetic component to the development of OCD, although this has not yet been proven. Based on a two-year MRI follow-up, we were able to show the self-limiting characteristics of juvenile osteochondrosis dissecans.

Fully automatic tracking of native knee kinematics from stereo-radiography with digitally reconstructed radiographs.

Precise measurement of joint-level motion from stereo-radiography facilitates understanding of human movement. Conventional procedures for kinematic tracking require significant manual effort and are time intensive. The current work introduces a method for fully automatic tracking of native knee kinematics from stereo-radiography sequences. The framework consists of three computational steps. First, biplanar radiograph frames are annotated with segmentation maps and key points using a convolutional neural network. Next, initial bone pose estimates are acquired by solving a polynomial optimization problem constructed from annotated key points and anatomic landmarks from digitized models. A semidefinite relaxation is formulated to realize the global minimum of the non-convex problem. Pose estimates are then refined by registering computed tomography-based digitally reconstructed radiographs to masked radiographs. A novel rendering method is also introduced which enables generating digitally reconstructed radiographs from computed tomography scans with inconsistent slice widths. The automatic tracking framework was evaluated with stereo-radiography trials manually tracked with model-image registration, and with frames which capture a synthetic leg phantom. The tracking method produced pose estimates which were consistently similar to manually tracked values; and demonstrated pose errors below 1.0 degree or millimeter for all femur and tibia degrees of freedom in phantom trials. Results indicate the described framework may benefit orthopaedics and biomechanics applications through acceleration of kinematic tracking.

Long-term clinical and radiological effectiveness and safety of ultralow doses of rituximab in rheumatoid arthritis: observational extension of the REDO trial.

BACKGROUND: The REDO trial (REtreatment with Rituximab in RhEmatoid arthritis: Disease Outcome after Dose Optimisation) showed similar disease activity for retreatment with ultralow doses (200 mg and 500 mg per 6 months) compared with standard low-dose rituximab (RTX, 1000 mg per 6 months). We performed an observational extension study of the REDO trial to assess long-term effectiveness. METHODS: Patients from the REDO trial were followed from start of the trial to censoring in April 2021. RTX use was at the discretion of patient and rheumatologist using treat to target. The primary outcome was disease activity (disease activity score in 28 joints C-reactive protein (DAS28-CRP)), analysed using a longitudinal mixed model by original randomisation and time-varying RTX dose. The original DAS28-CRP non-inferiority (NI) margin of 0.6 was used. RTX dose and persistence, safety and radiological outcomes were also assessed. FINDINGS: Data from 126 of 142 REDO patients was collected from 15 December 2016, up to 30 April 2021. Drop-outs continued treatment elsewhere (n=3) or did not consent (n=13).Disease activity did not differ by original randomisation group: 1000 mg mean DAS28-CRP (95% CI) of 2.2 (2.0 to 2.5), 500 mg 2.3 (2.1 to 2.4) and 200 mg 2.4 (2.2 to 2.5). Lower time-varying RTX dose was associated with higher DAS28-CRP (0.22 (95% CI 0.05 to 0.40) higher for 200 mg/6 months compared with 1000 mg/6 months), but remained within the NI-margin. RTX persistence was 93%. Median RTX dose was 978 mg (IQR 684-1413) per year, and no association was found between RTX dose and adverse events or radiological damage. INTERPRETATION: Long-term use of ultralow doses of RTX is effective in patients with rheumatoid arthritis responding to standard dose RTX.

[Application of Deep Learning to Diagnose and Classify Adolescent Idiopathic Scoliosis].

A deep learning-based model for automatic diagnosis and classification of adolescent idiopathic scoliosis has been constructed. This model mainly included key points detection and Cobb angle measurement. 748 full-length standing spinal X-ray images were retrospectively collected, of which 602 images were used to train and validate the model, and 146 images were used to test the model performance. The results showed that the model had good diagnostic and classification performance, with an accuracy of 94.5%. Compared with experts' measurement, 94.9% of its Cobb angle measurement results were within the clinically acceptable range. The average absolute difference was 2.1°, and the consistency was also excellent (r2≥0.9552, P<0.001). In the future, this model could be applied clinically to improve doctors' diagnostic efficiency.